



'''simple docstring'''




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class       _lowerCAmelCase				(     _A					):
	__SCREAMING_SNAKE_CASE  :						str						     =   42







class       _lowerCAmelCase				(     _A ,     _A					):


	@register_to_config
	def __init__(self			, lowercase = 65536			, lowercase = None			, lowercase = 2			, lowercase = 2			, lowercase = 0			, lowercase = "fourier"			, lowercase = True			, lowercase = False			, lowercase = 0.0			, lowercase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			, lowercase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			, lowercase = "UNetMidBlock1D"			, lowercase = None			, lowercase = (32, 32, 64)			, lowercase = None			, lowercase = 8			, lowercase = 1			, lowercase = False			, ):
				super().__init__()
				A_      :		Optional[Any]      =	sample_size

				# time
				if time_embedding_type == "fourier":
							A_      :		Optional[Any]      =	GaussianFourierProjection(
							    embedding_size=8			, set_W_to_weight=a_			, log=a_			, flip_sin_to_cos=a_				)
							A_      :		Tuple      =	2 * block_out_channels[0]
				elif time_embedding_type == "positional":
							A_      :		List[str]      =	Timesteps(
							    block_out_channels[0]			, flip_sin_to_cos=a_			, downscale_freq_shift=a_				)
							A_      :		List[Any]      =	block_out_channels[0]

				if use_timestep_embedding:
							A_      :		Union[str, Any]      =	block_out_channels[0] * 4
							A_      :		int      =	TimestepEmbedding(
							    in_channels=a_			, time_embed_dim=a_			, act_fn=a_			, out_dim=block_out_channels[0]			, )

				A_      :		Any      =	nn.ModuleList([]				)
				A_      :		Any      =	None
				A_      :		int      =	nn.ModuleList([]				)
				A_      :		Dict      =	None

				# down
				A_      :		Optional[int]      =	in_channels
				for i, down_block_type in enumerate(a_				):
							A_      :		Optional[Any]      =	output_channel
							A_      :		Dict      =	block_out_channels[i]

							if i == 0:
										input_channel += extra_in_channels

							A_      :		Tuple      =	i == len(a_				) - 1

							A_      :		Any      =	get_down_block(
							    a_			, num_layers=a_			, in_channels=a_			, out_channels=a_			, temb_channels=block_out_channels[0]			, add_downsample=not is_final_block or downsample_each_block			, )
							self.down_blocks.append(a_				)

				# mid
				A_      :		Union[str, Any]      =	get_mid_block(
				    a_			, in_channels=block_out_channels[-1]			, mid_channels=block_out_channels[-1]			, out_channels=block_out_channels[-1]			, embed_dim=block_out_channels[0]			, num_layers=a_			, add_downsample=a_			, )

				# up
				A_      :		Optional[int]      =	list(reversed(a_				)				)
				A_      :		Tuple      =	reversed_block_out_channels[0]
				if out_block_type is None:
							A_      :		List[str]      =	out_channels
				else:
							A_      :		Union[str, Any]      =	block_out_channels[0]

				for i, up_block_type in enumerate(a_				):
							A_      :		Optional[int]      =	output_channel
							A_      :		Dict      =	(
							    reversed_block_out_channels[i + 1] if i < len(a_				) - 1 else final_upsample_channels
							)

							A_      :		Dict      =	i == len(a_				) - 1

							A_      :		List[str]      =	get_up_block(
							    a_			, num_layers=a_			, in_channels=a_			, out_channels=a_			, temb_channels=block_out_channels[0]			, add_upsample=not is_final_block			, )
							self.up_blocks.append(a_				)
							A_      :		Any      =	output_channel

				# out
				A_      :		Union[str, Any]      =	norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			, 32				)
				A_      :		Tuple      =	get_out_block(
				    out_block_type=a_			, num_groups_out=a_			, embed_dim=block_out_channels[0]			, out_channels=a_			, act_fn=a_			, fc_dim=block_out_channels[-1] // 4			, )


	def        _a      (self			, lowercase			, lowercase			, lowercase = True			, ):
				A_      :		List[Any]      =	timestep
				if not torch.is_tensor(a_				):
							A_      :		str      =	torch.tensor([timesteps]			, dtype=torch.long			, device=sample.device				)
				elif torch.is_tensor(a_				) and len(timesteps.shape				) == 0:
							A_      :		Optional[Any]      =	timesteps[None].to(sample.device				)

				A_      :		Dict      =	self.time_proj(a_				)
				if self.config.use_timestep_embedding:
							A_      :		Optional[Any]      =	self.time_mlp(a_				)
				else:
							A_      :		Any      =	timestep_embed[..., None]
							A_      :		List[str]      =	timestep_embed.repeat([1, 1, sample.shape[2]]				).to(sample.dtype				)
							A_      :		Tuple      =	timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])				)

				# 2. down
				A_      :		Optional[Any]      =	()
				for downsample_block in self.down_blocks:
							A_      :		Optional[int]      =	downsample_block(hidden_states=a_			, temb=a_				)
							down_block_res_samples += res_samples

				# 3. mid
				if self.mid_block:
							A_      :		List[str]      =	self.mid_block(a_			, a_				)

				# 4. up
				for i, upsample_block in enumerate(self.up_blocks				):
							A_      :		List[str]      =	down_block_res_samples[-1:]
							A_      :		Optional[int]      =	down_block_res_samples[:-1]
							A_      :		str      =	upsample_block(a_			, res_hidden_states_tuple=a_			, temb=a_				)

				# 5. post-process
				if self.out_block:
							A_      :		Any      =	self.out_block(a_			, a_				)

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=a_				)






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GraphormerForGraphClassification""",
					    """GraphormerModel""",
					    """GraphormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_graphormer import (
										    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GraphormerForGraphClassification,
										    GraphormerModel,
										    GraphormerPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



